from numpy import *
def loadDataSet():
    postingList=[['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],
             ['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],
             ['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],
             ['stop', 'posting', 'stupid', 'worthless', 'garbage'],
             ['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'],
             ['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']] 
    classVec = [0,1,0,1,0,1]
    return postingList,classVec
def createVocabList(dataSet):
    vocabSet = set([]) 
    for document in dataSet: 
        vocabSet = vocabSet | set(document) 
    return list(vocabSet)
def setOfWords2Vec(vocabList, inputSet):
    returnVec = [0] * len(vocabList) 
    for word in inputSet: 
        if word in vocabList: 
            returnVec[vocabList.index(word)] = 1
        else:
            print ("the word: %s is not in my Vocabulary!" )% word 
    return returnVec 
def trainNB0(trainMatrix,trainCategory):
    numTrainDocs = len(trainMatrix)
    numWords = len(trainMatrix[0])
    pAbusive = sum(trainCategory)/float(numTrainDocs) 
    p0Num = zeros(numWords); p1Num = zeros(numWords)
    p0Denom = 0.0; p1Denom = 0.0
    for i in range(numTrainDocs):
        if trainCategory[i] == 1:
            p1Num += trainMatrix[i]
            p1Denom += sum(trainMatrix[i])
        else:
            p0Num += trainMatrix[i]
            p0Denom += sum(trainMatrix[i])
    p1Vect= p1Num/p1Denom #change to log()
    p0Vect= p0Num/p0Denom #change to log()
    return p0Vect,p1Vect,pAbusive
def textParse(bigstring):
    import re
    listofTokens= re.split(r'\W*', bigstring)
    return [tok.lower() for tok in listofTokens if len(tok)>2]
    
def spamTest():
    docList=[];classList = [];fullText =[]
    for i in range(1,26):
    wordList = textParse(open('email/spam/d.txt'g i).read()) 
    docList.append(wordList)
    fullText.extend(wordList)
    classList.append(1)
    wordList = textParse (open('email/ham/gd.txt' i).read()) 
    docList.append(wordList) 
    fullText.extend(wordList)
    classList.append(0)
vocabList = createVocabList(docList)
trainingSet = range(50); testSet=[]
for i in range(10):
    randIndex = int(random.uniform(0,len(trainingSet))) 
    testSet.append(trainingSet[randIndex])
    del(trainingSet [randIndex])
trainMat=[]; trainClasses =[]
for docIndex in trainingSet:
    trainMat.append(setOfWords2Vec(vocabList, docList [docIndex])) 
    trainClasses.append(classList [docIndex])
p0V,p1V,pSpam = trainNB0 (array(trainMat),array(trainClasses)) 
errorCount =0
for docIndex in testSet:
    wordVector = setOfWords2Vec(vocabList, docList [docIndex]) 
    if classifyNB(array(wordVector),p1V,p1V,pSpam) != classList [docIndex]: 
        errorCount+= 1
print ('the error rate is: ',float(errorCount)/len(testSet))